M
Mathieu Gautier
Researcher at SupAgro
Publications - 125
Citations - 6794
Mathieu Gautier is an academic researcher from SupAgro. The author has contributed to research in topics: Population & Gene. The author has an hindex of 37, co-authored 120 publications receiving 5631 citations. Previous affiliations of Mathieu Gautier include Institut national de la recherche agronomique & University of Montpellier.
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Journal ArticleDOI
DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data
Jean-Marie Cornuet,Pierre Pudlo,Julien Veyssier,Alexandre Dehne-Garcia,Mathieu Gautier,Raphaël Leblois,Jean-Michel Marin,Arnaud Estoup +7 more
TL;DR: DIYABC v2.0 implements a number of new features and analytical methods, including efficient Bayesian model choice using linear discriminant analysis on summary statistics and the serial launching of multiple post-processing analyses.
Journal ArticleDOI
rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure
Mathieu Gautier,Renaud Vitalis +1 more
TL;DR: The R package rehh provides a versatile tool to detect the footprints of recent or ongoing selection with several graphical functions that help visual interpretation of the results.
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Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates.
TL;DR: This study investigates several modeling extensions to improve the estimation accuracy of the population covariance matrix and all the related measures and defines a robust Bayesian framework to characterize adaptive genetic differentiation across populations.
Journal ArticleDOI
Reliable ABC model choice via random forests.
Pierre Pudlo,Jean-Michel Marin,Arnaud Estoup,Jean-Marie Cornuet,Mathieu Gautier,Christian P. Robert,Christian P. Robert +6 more
TL;DR: This work proposes a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms, modifying the way Bayesian model selection is both understood and operated.
Journal ArticleDOI
The effect of RAD allele dropout on the estimation of genetic variation within and between populations
Mathieu Gautier,Karim Gharbi,Timothee Cezard,Julien Foucaud,Carole Kerdelhué,Pierre Pudlo,Pierre Pudlo,Jean-Marie Cornuet,Arnaud Estoup +8 more
TL;DR: It is found that ADO tends to overestimate genetic variation both within and between populations, and possible solutions to filter the most problematic cases of ADO using read coverage to detect markers with a large excess of null alleles are discussed.